1. Field of the Invention
The invention relates generally to biochemical reaction networks and more specifically to reconstruction of metabolic networks in an organism to obtain optimal desired whole cell properties.
2. Background Information
Genome sequencing and annotation technologies are giving us detailed lists of the molecular components that cells are comprised of, and high-throughput technologies are yielding information about how these components are used. Thus we are approaching the stage where biological design is possible on a genome scale. It has proven difficult to ‘splice’ one gene from one organism into another and produce predictable results. The primary reason is that every component in a living cell has been honed through a lengthy evolutionary process to ‘fit’ optimally into the overall function of the cell. Simply introducing a foreign gene, or deleting an existing gene does not lead to predictable nor optimal results. Methods are needed to a priori predict the consequences of single or multiple gene deletions or additions on the function of an entire cellular function and force the remaining components to function in a predetermined manner. Such methods are lacking, although limited progress has been made with metabolic function on a cellular scale.
The interest in the redirection of metabolic fluxes for medical and industrial purposes has existed for some time. As a result of this interest, the field of metabolic engineering has been born, and the primary goal of metabolic engineering is to implement desirable metabolic behavior in living cells. Advances and applications of several scientific disciplines including computer technology, genetics, and systems science lie at the heart of metabolic engineering.
The traditional engineering approach to analysis and design utilizes a mathematical or computer model. For metabolism this would require a computer model that is based on fundamental physicochemical laws and principles. The metabolic engineer then hopes that such models can be used to systematically ‘design’ a new and improved living cell. The methods of recombinant DNA technology should then be applied to achieve the desired cellular designs.
The 25–30 year history of metabolic analysis has demonstrated the need to quantitate systemic aspects of cellular metabolism, (see e.g., Fell D., Understanding the control of metabolism, (London, Portland Press) (1996); Heinrich R., et al., Metabolic regulation and mathematical models, Progress in Biophysics and Molecular Biology, 32:1–82, (1977); Heinrich R. and Schuster S., The regulation of cellular systems, (New York, Chapman & Hall), xix, p. 372 (1996); Savageau M. A., Biochemical systems analysis. I. Some mathematical properties of the rate law for the ecomponent enzymatic reactions, J. Theor. Biol. 25(3):365–69 (1969)). There are significant incentives to study metabolic dynamics. A quantitative description of metabolism and the ability to produce metabolic change is not only important to achieve specific therapeutic goals but has general importance to our understanding of cell biology. Important applications include strain design for the production of therapeutics and other biochemicals, assessment of the metabolic consequences of genetic defects, the synthesis of systematic methods to combat infectious disease, and so forth. Quantitative and systemic analysis of metabolism is thus of fundamental importance. However, a review in the field has concluded that “despite the recent surge of interest in metabolic engineering, a great disparity still exists between the power of available molecular biological techniques and the ability to rationally analyze biochemical networks” (Stephanopoulos G., Metabolic engineering. Current Opinions in Biotechnology, 5:196–200 (1994)). Although this statement is a few years old, it still basically holds true. This conclusion is not surprising for we are competing with millions of years of natural evolution that achieves the best fitness of an organism in a given environment.
Although partial gene regulatory networks containing a small number of reactions have been designed (reviewed in Hasty et al., Computational studies of gene regulatory networks: In numero molecular biology, Nature, 2: 268–79 (2001)), the a priori design of biochemical regulatory networks, such as metabolic networks with defined performance characteristics and their subsequent construction has not been reduced to practice. The primary reason is that reliable detailed kinetic models cannot be constructed for an entire metabolic network, mainly because there are too many kinetic parameters whose numerical values must be determined and the detailed kinetic equations are by-and-large unknown. Thus, a priori design of optimal biochemical reaction networks, such as metabolism, is not possible because predictive kinetic models cannot be achieved. In fact, the values of the kinetic constants change with time due to mutations and an evolutionary process.
Heretofore it has been impossible to predict the end point of evolutionary processes as they are expected to be the outcome of the selection from random events. This invention discloses a method that allows for the a priori calculation of the endpoint of the evolution of metabolic networks in a defined environment. Although there are other mathematical modeling methods that are based on optimization principles in biological systems; i.e. the cybernetic modeling approach (Varner J. and Ramkrishna D., “Mathematical models of metabolic pathways,” Curr. Opin. Biotechnol., 10(2):146–50, (1999)), they are not amenable to the design of biological networks due to the number of parameters required. It thus gives the basis for the use of an evolutionary process to create or build such designs.